DataQi

Finding life force, hidden in your data. 01010101010101010101010101010101010101010101010101010101010101010101010010101Qi01010101010101010101010101010101010101

[Marcus Borba @marcusborba]

Japanese scientists have create #AI that can visualize our thoughts
#ArtificialIntelligence #MachineLearning #BigData #DataScience #DeepLearning #Robotics

[National Data Quality Forum @NDQFonline]

All you machine learning wizards, show us your magic to improve data quality! Apply to #Data2thon!
For more info, visit: https://t.co/YxG7pYgnV9
#Dataquality #DataQthon #Dataanalysis
@Pop_council @dataQi @IDInsight @ICMR_NIMS @WHOSEARO @UN_SDG @iitdelhi @IEGResearch

[Sebastian Ruder @seb_ruder]

This is a great post that highlights the connection between the building blocks used in Transformers and Capsule Networks. Definitely worth reading!

[Sebastian Ruder @seb_ruder]

New paper with @mattthemathman & @nlpnoah on adapting pretrained representations: We compare feature extraction & fine-tuning with ELMo and BERT and try to give several guidelines for adapting pretrained representations in practice. https://t.co/yXcoLk6MkG

[Andrew Ng @AndrewYNg]

Announcing https://t.co/Ryb1M38abX's newest specialization, TensorFlow: From Basics to Mastery! In partnership with @TensorFlow team. To be great at implementing AI, you need to know how to best use ML frameworks like TF. @deeplearningai_ Take Course 1: https://t.co/Aa9WZLYsNq

[Denny Britz @dennybritz]

“Machine-learning techniques used by scientists to analyse data are producing results that are misleading and often completely wrong, […] identifying patterns that exist only in that data set and not the real world. ” 🔥

https://t.co/ZmnVHpqpjE

[Denny Britz @dennybritz]

ML is more easily accessible than ever before. But with great power comes great responsibility.

The real danger of ML is that it’s now easier to justify whatever you like by pointing to the output of an ML model w/o understanding the big assumptions underlying your experiments.

[Denny Britz @dennybritz]

OpenAI’s new language model just paved the way to AGI:

While true, repeat:
1. Generate N * 1.01 samples
2. Retrain the model on the generated samples

Don’t try at home.

[François Chollet @fchollet]

Designing the system and writing the code is the easy part. What's hard is delivering what people want, staying focused, and long-term community-building.

[Christopher Manning @chrmanning]

People most-cited by #AAAI papers shows 25 years of AI history. 1990s greats: Pearl—Kautz—Weld—Selman; rise&fall: Comitzer—Sandholm—Sutton—Domingos—Tambe—Littman—Jordan—Veloso—Koller—Boutilier—Ng—Barto; 2010s neural boomers: Bengio—Sutskever—Hinton—Manning https://t.co/YdBo94tATl

[Fei-Fei Li @drfeifei]

Stanford’s AI-assisted Healthcare is calling for research abstract submission and participating in an upcoming fall conference on #AI and healthcare, partnered with @NatureMedicine More details here: https://t.co/kg4fz3aSqH

[Papers with Code @paperswithcode]

We’ve just released the new Papers With Code! Site now has over 950+ ML tasks, 500+ evaluation tables (including state of the art results) and 8500+ papers with code. Explore the resource here: https://t.co/stfzzn0IfM. Have fun!

[allen institute @AllenInstitute]

Our Institute for Brain Science recently released the first dataset from live human neurons. This #openscience resource contains electrophysiological, morphological, and transcriptomic properties gathered from individual cells, and models simulating cell activity. #BrainWeek

[Gael Varoquaux 🦋 @GaelVaroquaux]

Announcing IMPAC: an IMaging-PsychiAtry Challenge, using data-science to predict autism from brain imaging
https://t.co/BjayWTTmz4

9000€ of prizes to win! More than 2000 individuals scanned!

Organized by @SaclayCDS and @R3RT0's team at @institutpasteur

[Ian Hussey @ianhussey]

Psychology/psychiatry has to get better at using cross validation methods. Not training and testing on the same data is data science 101.

[Dani S. Bassett @DaniSBassett]

Just out on PsyArxiv, "Digital phenotyping for psychiatry: Accommodating data and theory with network science methodologies" led by @DLydonStaley w/@sattertt and Ian Barnett. https://t.co/n2915F4ZEa Inspired by the opportunities for future work in this area!

[François Chollet @fchollet]

Programming isn't like playing an instrument. Micro skills (like typing) matter little compared to macro skills (problem-solving, adaptivity, abstraction...)

That's why, by the time you're 30, it doesn't matter if you learned programming at age 5, 15, or 25.

[François Chollet @fchollet]

I see lots of people stating unequivocally that the deep learning boom started with Krizhevsky et al 2012. But I see little credit given to Ciresan et al, who were winning image classification competitions in 2011 with deep convnets implemented in CUDA, trained on NVIDIA GPUs